1 intro

1.1 Purpose

  1. Validate trialwise ub55 Stroop GLM high-vs-low contrast.
  2. Explore methods of modeling trial-wise parcel-mean beta estimates.

1.2 Notes on analyses

GLMs

  • DMCC55B
  • trialwise LS-A, fix-shaped BLOCK(1,1), Stroop

Contrasts (on regional means):

  • Stroop: \((\text{PC50InCon} + \text{biasInCon} - \text{PC50Con} - \text{biasCon})/2\)

Plotting and statistical details:

2 quick look

2.1 raw data

3 prelim models

## # A tibble: 182 x 9
## # Groups:   term [1]
##    parcel    estimate     se statistic  p.value term      p.fdr hemi  num.roi
##    <chr>        <dbl>  <dbl>     <dbl>    <dbl> <chr>     <dbl> <chr>   <int>
##  1 LH_Vis_1    0.0933 0.0162      5.75 8.99e- 9 stroop 6.79e- 8 L           1
##  2 LH_Vis_3    0.133  0.0203      6.54 6.30e-11 stroop 7.91e-10 L           3
##  3 LH_Vis_6    0.131  0.0301      4.33 1.46e- 5 stroop 5.45e- 5 L           6
##  4 LH_Vis_8    0.125  0.0204      6.13 8.91e-10 stroop 9.90e- 9 L           8
##  5 LH_Vis_12   0.125  0.0273      4.59 4.53e- 6 stroop 1.81e- 5 L          12
##  6 LH_Vis_13   0.0795 0.0255      3.11 1.85e- 3 stroop 4.61e- 3 L          13
##  7 LH_Vis_14   0.122  0.0202      6.05 1.46e- 9 stroop 1.43e- 8 L          14
##  8 LH_Vis_15   0.0738 0.0205      3.60 3.22e- 4 stroop 9.75e- 4 L          15
##  9 LH_Vis_16   0.114  0.0262      4.33 1.47e- 5 stroop 5.45e- 5 L          16
## 10 LH_Vis_18   0.104  0.0226      4.61 4.04e- 6 stroop 1.65e- 5 L          18
## # … with 172 more rows

3.1 Brains

  • t-values displayed; from HLM fitted to TR-level data (see intro)
  • colors are reversed (black = high, yellow = low) so large positive effects can be seen on white underlay.

3.1.1 bias_stroop

3.1.2 pc50_stroop

3.1.3 stroop

3.1.4 pc50_bias